The confounding problem of private data release

In our Big Data era, as data-driven decision making sweeps through all aspects of society, the demands to make useful data available are growing ever louder. For example, the ubiquity of GPS-enabled devices has resulted in a wealth of data about the movements of individuals and populations, which can be analyzed for useful information to aid in city and traffic planning, disaster preparedness, and so on. But the problem of releasing such data without disclosing confidential information, such as the places people visit, is a subtle and difficult one. Is “private data release” an oxymoron? This talk will delve into the motivations of private data release, explore the challenges, and outline some of the historical and recent approaches developed in response to this confounding problem.

Divesh Srivastava is the head of Database Research at AT&T Labs-Research. He is a Fellow of the Association for Computing Machinery (ACM), the Vice President of the VLDB Endowment, and the managing editor of the Proceedings of the VLDB Endowment (PVLDB). He has served as an associate editor of the ACM Transactions on Database Systems (TODS), and as an associate Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (TKDE). He has presented keynote talks at several international conferences, and his research interests and publications span a variety of topics in data management. He received his Ph.D. from the University of Wisconsin, Madison, USA, and his Bachelor of Technology from the Indian Institute of Technology, Bombay, India.

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